Multi-Modality Image Manipulation Detection
Chao Yang, Zhiyu Wang, Huawei Shen, Huizhou Li, Bin Jiang
Abstract
State-of-the-art multi-stream methods for image manipulation detection suffer from gaps between features. Moreover, the rapid development of GANs makes it an emerging method of image tampering. However, existing natural scene image tampered datasets are limited to manual tampering. In this paper, we address these two issues. Firstly, we propose a novel two-stream multi-modality image manipulation detection model (MM-net) that abandons the way of fusion. The main idea of the proposed model is to exploit one stream to guide the learning of the other stream through attention mechanism. Therefore, our approach enjoys the benefits of the multi-stream methods while avoiding the semantic gaps caused by bridging gaps between different streams. Secondly, we build the first tampered dataset of natural images based on GANs, pushing manipulation detection toward more realistic and challenging scenarios. Extensive experimental results demonstrate that our model outperforms state-of-the-art approaches on both manually and GANs tampered images.